National project examines online learning patterns

UH is partnering with 15 other higher education institutions to pool data in order to create benchmarks and models for student success in online learning.

The University of Hawaiʻi System is participating in a national project to bring the power of big data to online learning.

UH and 15 other higher education institutions will share practices, intervention strategies and anonymized records to look for patterns that predict success in online learning through the Predictive Analytics Reporting Framework project, or PAR.

UH was one of the six founding PAR partners that have now expanded the project with an additional 10 colleges and universities. PAR utilizes “big data” exploratory techniques to look for patterns that identify causes of student loss and momentum in online learning.

UH participation in the project is being led by Hae Okimoto, director of academic technologies in Information Technology Services, and Pearl Imada-Iboshi, UH System director of institutional research and analysis.

“Working with the PAR project is providing us an opportunity to work with some of the best minds in the country and address problems at a scale that no single institution could do on its own,” said Okimoto. “We have already learned that if a student needs to take developmental courses upon entering community college, they will be more successful if they do not take more than one online course in their first semester.”

Imada-Iboshi added, “Our work as one of the initial PAR partners has enhanced our ability to perform analyses that help advance student success and institutional effectiveness.”

Creating benchmarks for success in online learning

The institutions will be voluntarily pooling fully anonymized student and course level data from student information systems and learning management systems into an institutionally de-identified database that will be used to create models predicting student success and momentum while in pursuit of a higher education credential.

With 16 institutions, over 1 million student and 6 million course level records, PAR has a truly unique opportunity to benchmark and model across institutions.

Using this data, the PAR Framework will help predict risks to student retention and progress toward degree completion, and will allow institutions to remove obstacles to student success and demonstrably improve rates of student retention and degree completion.

The project plans to offer institutions easy to understand resources, including reports, dashboards and decision-support tools, to anticipate threats to student achievement, and remove barriers to student success before they become problems.